Two key obstacles to efficient experimental therapeutics for cognitive impairment (CI) associated with Parkinson's disease (PD) are lack of accurate prediction of which patients are at highest risk for CI in the near term, and heterogeneity in the pathology underlying CI in PD and Parkinson's disease dementia (PDD). Project 1 will address both of these critical issues. The first goal of the project is to improve the ability to predict which patients with PD are at highest risk for developing CI by building a predictive model that combines clinical and biomarker data. The second goal of the project is to better understand potential pathological subtypes of dementia in PDD and Lewy Body Dementia (LBD) by examining patterns of expression of clinical and biomarker features which may reflect different underlying pathology. The specific aims of Project 1 are: 1) to replicate previously-reported candidate biomarkers of CI in a training cohort of LBSD patients; 2) to define relationships among candidate biomarkers in Aim 1 to identify potential pathophysiological subtypes of cognitive impairment in PD and DLB; and 3) to develop a multimodal predictive algorithm for cognitive decline in PD and apply it to an independent test cohort of PD patients. We will accomplish these aims in the context of longitudinal cohort study that uses data already collected in the first funding cycle of the Penn Udall Center along with additional data collected in this cycle. We will use Hierarchical Cluster Analysis and Principal Components Analysis to examine patterns of clinical and biomarker expression. We will use a split-sample approach to developing and testing the predictive algorithm. The products of this study will be a deeper understanding of the inter-relations of clinical and biological features of CI in PD and a practical tool for assessing the risk of developing this disabling complication.